Recommender systems play an important role in organizing the information available to us, by deciding which content should be exposed to users and how it should be prioritized. These decisions impact both the users and the item producers of the platform. While recommender systems are usually designed to maximize performance metrics of user satisfaction, several audits recently revealed potential performance disparities across users [1]}, [2]}, [3]}, [4]}. On the side of item producers, the growing literature on fairness of exposure aims to avoid popularity biases [5]} by reducing inequalities in the exposure of different items [6]}, or aiming for equal exposure weighted by relevance [7]}, [8]}, [9]}. In most cases, the approaches proposed for user- and item-side fairness aim to reduce inequalities.
